Postgraduate Courses

Emergent innovations in autonomy, connectivity and shared mobility are revolutionizing vehicular traffic systems. Developing comprehensive and systematic understandings of traffic dynamics is essential to drive these innovations to reinvent transportation systems. The course covers different aspects of vehicular traffic flow dynamics and how to describe and simulate them with mathematical models. This course starts with how to obtain and interpret traffic flow data, the basis of any quantitative traffic modeling. The second and main part of this course introduces different approaches and models to mathematically describe vehicular traffic flow, and their application in simulation from microscopic to macroscopic level. The last part of this course introduces major applications of traffic flow theory including traffic flow management schemes, mix-autonomy traffic flow modeling and advanced control and sensing strategies by connected and automated vehicles.

This course discusses formulations and algorithms of finding equilibrium flow patterns through transportation networks to assess the network performance and providing foresights into the future dynamics, then explores policies, designs, and strategies to improve the system performance. Emphasis is placed on the understanding of the paradigm of equilibrium analysis of transportation systems. Topics discussed in the class include static user equilibrium, system optimum, market- based instruments for congestion management, bi-level programming models, bottleneck models dynamic network modeling, and the implications of emerging technologies on the analyses of transportation networks.

This course will introduce important optimization problems arising from transport and logistics management, including network flow problems, routing and scheduling problems, and problems involving uncertainty. It will focus on modeling techniques and solution methodology for problem solving. Theoretical and operational insights into the problems will also be discussed. The goal of the course is to train students to a level of technical competency to formulate and solve related optimization problems that they may encounter in both research and real-life.

This course will introduce traffic control system concepts, components, algorithms, and tools for evaluating their effectiveness. With the instruction, assignments, and projects in this course, students are expected to learn about traffic system control devices, working principles, and popular algorithms. Additionally, the VISSIM traffic simulation package will be introduced in greater detail so that students can use it for evaluating the performance of traffic operation plans.

Intelligent Transportation Systems (ITS) apply a variety of technologies to monitor, evaluate, and manage transportation systems to enhance their efficiency, safety, and sustainability. This postgraduate-level course introduces the basic functional components in ITS and how they are designed and operated to manage multi-modal transportation systems., including both passenger and freight transportation systems and infrastructure. The course topics cover the following three main parts: (i) Emerging vehicle technologies and mobility services, data management, and institutional issues; (ii) Multi-modal freight operations and management in road, rail, maritime and inter-modal systems; (iii) Analytical methods including fundamentals of system analysis, risk and regression analysis, network flow optimization and algorithm design, etc.

This postgraduate-level course introduces how game-theoretical methods are used to model strategic behaviors and to support decision making in transportation systems. Fundamental knowledge in game theory and mechanism design, including different game representations, equilibrium concepts and information asymmetry will first be covered. Variational inequality will then be introduced, with an emphasize of its importance in determining equilibrium solutions for transportation network models.

This course aims to develop students’ fundamental understanding of the application scenarios, challenges, and solutions of wireless connectivity in various systems involving autonomous things, and under possible mobility. Topics covered include fundamentals of digital communications, future wireless connectivity requirements, and various solutions to the unique challenges such as dynamic propagation environment, scalability, complexity, and heterogeneity.

This course will introduce modern concepts, algorithms, and tools for data-driven transportation modeling and optimization. By taking this course, students will have the chance to master emerging data-driven methods for transportation systems modeling and optimization.

Intelligent connected vehicles (ICVs) are believed to change people’s life in the near future by making the transportation safer, cleaner and more comfortable. Although many prototypes of ICVs have been developed to prove the concept of autonomous driving and the feasibility of improving traffic efficiency, there still exists a significant gap before achieving mass production of high-level ICVs. This course aims to present an overview of both the state of the art and future perspectives of key technologies that are needed for future ICVs. Through the study of this course, students will understand and master the basic concepts, key technologies and applications of ICV, and initially learn and master the ability to use that knowledge to solve practical problems, especially in cross-disciplinary communication and transportation context.

The course aims to help students master the basic concepts and research methods of Artificial Intelligence (AI) and machine learning, understand future development trends, and lay the foundation for further research in leveraging machine learning and AI in transportation research. Through the study of this course, students will understand and master the basic concepts, ideas and methods of AI and related machine learning techniques, and initially learn and master the ability to use those machine learning techniques to solve practical problems, especially in transportation context.

The course will cover a wide range of engineering psychology topics as well as how the research in these directions can affect policies and regulations in vehicle design and surface transportation. The students will gain an understanding of the characteristics and limitations of human beings from engineering psychology perspectives of view and how the design of traffic control devices, the roadway, the in-vehicle devices, regulations and traffic rules can be affected by the research in these directions.

This course introduces methods for analysis and control design of nonlinear systems, which have a wide range of engineering applications including transportation, robotics, biology, energy, and manufacturing systems. The course includes: 1) Mathematical models of nonlinear systems, and fundamental differences between the behavior of linear and nonlinear systems, equilibrium, limit cycles and general invariant sets. 2) Phase plane analysis, Lyapunov stability, Input-to-state stability, Input-output stability, and approximation methods. 3) Feedback linearization and nonlinear control design tools, including Lyapunov-based control and Backstepping. From learning the nonlinear phenomena to understanding the mathematical properties and then analyzing system behaviors, students will be able to grasp the fundamental concepts and advanced tools that are useful in the analysis of nonlinear systems. The control design tools for nonlinear systems from feedback linearization to advanced backstepping control are covered in this course. Students will be proficient in skills of independently assessing the advantages and disadvantages of different nonlinear methods, make a qualified choice of method for analysis and design of nonlinear control systems that arise from various research areas.

Linear and integer programming are powerful decision optimization techniques that have been applied for decision support in almost all walks of life. This course will explore the fundamental theories and methodologies of linear and integer programming and demonstrate how these techniques can be used to solve practical problems. The first part of this course, linear programming, explores the simplex algorithm and the duality theory that act as the cornerstones of modern linear and integer programming solvers. The second part, integer programming, covers a broader range of topics in both methodology and applications, including problem modeling, model analysis, and decomposition- and relaxation-based solution methods. Implementation issues and industry cases will also be discussed. The goal of this course is to train students to a level of technical competency to appreciate and understand literature and apply various solution methods for problem solving.

This course aims to develop students’ fundamental understanding of the theory and application of incremental learning and adaptive signal processing. Topics covered in this course include Wiener filter, least mean squares (LMS), recursive least squares (RLS), the Kalman filter, classification, parameter learning, neural network and deep learning.

The course will cover a wide range of analytical methods used in human factors research domain. The students will gain an understanding of the procedures, objectives and limitations of different research methods. The course will also include four case studies so that students would gain first-hand experience in applying the methods in real projects. These contents are required for research investigating users’ behaviors.

This postgraduate level course aims to introduce practical modeling methods based on theories and principles in applied mathematics, operations research, and management science for solving the planning, design and evaluation of complex transportation systems, including both passenger logistics and freight distribution systems. This course will cover the three major aspects: inventory management, network design and flow optimization, as well as facility location in a logistical system. It introduces fundamental concepts, model formulation, optimization techniques, as well as solution algorithms (including stochastic process, network/graphic representation, classic OR problems, formulation of optimization problems, exact solution methods, meta heuristics, continuous approximation, etc.). It will also cover practical solution approaches that reduce cumbersome details of transportation systems into models with a manageable number of parameters and decision variables. A variety of perspectives and techniques to both classic problems and recent advances will be presented along with ways to compare their performance.

This course aims to introduce multi-modal (rail, road, waterway, etc.) freight transportation operations and infrastructure systems. It comprises four inter-connected parts: 1) introduce basic modal-specific concepts and industry development; 2) explain widely used modeling techniques used in the multi-modal and inter-modal freight systems; 3) introduce transportation infrastructure management for different modes; and 4) apply the methodologies to emerging high-profile transportation research topics (e.g., resilience planning) through a term project.

Selected topics in intelligent transportation of current interest in emerging areas and not covered by existing courses. May be repeated for credit if different topics are covered.

Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F.

An independent study on selected topics carried out under the supervision of a faculty member.

Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.